The representation of 3D lane has a significant impact on the results of 3D lane detection. Most of the previous methods are based on 3D anchor to represent 3D lanes[1]}, [2]}, [3]}. However, 3D anchor design is difficult and ineffective. Different from 3D anchor and other design methods, we propose a method to predict 3D lanes in the bird-eye-view plane by referring to the YOLO[4]} and LaneNet[5]}. As shown in Figure.REF , we divide the BEV plane \(P_{road}\)  which is the plane with \(z=0\)  in the road coordinates \(\mathcal {C}_{road}=\left(x,y,z\right)\)  into several cells. Each cell represents \(x \times x\) (\(x\)  defaults to 0.5\(m\) ). We directly predict the four heads with the same resolution, including the confidence, the embedding used for clustering, the offset from the cell center to the lane in row direction and the average height of each cell. Unlike YOLO, we get uncoupled heads.
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